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The Common Factor in Idiosyncratic Volatility

Author

Listed:
  • Stijn Van Nieuwerburgh

    (NYU Stern School of Business)

  • Hanno Lustig

    (Anderson School of Business)

  • Bryan Kelly

    (University of Chicago)

  • Bernard Herskovic

    (New York University)

Abstract
We show that firms' idiosyncratic volatility in returns and cash flows obeys a strong factor structure. We find that the stocks of firms with large, negative common idiosyncratic volatility (CIV) factor betas earn high average returns. The CIV beta quintile spread is 6.4% per year. To explain this spread, we develop a heterogeneous investor model with incomplete markets in which the idiosyncratic volatility of investor consumption growth inherits the factor structure of firm cash flow growth. In our model, the CIV factor is a priced state variable, because an increase in volatility represents a worsening of the investment opportunity set for the average investor. The calibrated model is able to match the high degree of comovement in idiosyncratic volatilities, the CIV beta spread, along with a host of asset price moments.

Suggested Citation

  • Stijn Van Nieuwerburgh & Hanno Lustig & Bryan Kelly & Bernard Herskovic, 2014. "The Common Factor in Idiosyncratic Volatility," 2014 Meeting Papers 810, Society for Economic Dynamics.
    • Handle: RePEc:red:sed014:810
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    References listed on IDEAS

    as
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    Cited by:

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    2. Bartram, Söhnke M. & Brown, Gregory W. & Stulz, René M., 2016. "Why does idiosyncratic risk increase with market risk?," CFS Working Paper Series 533, Center for Financial Studies (CFS).
    3. Bernard Herskovic, 2015. "Networks in Production: Asset Pricing Implications," 2015 Meeting Papers 378, Society for Economic Dynamics.
    4. Christoffersen, Peter & Lunde, Asger & Olesen, Kasper V., 2019. "Factor Structure in Commodity Futures Return and Volatility," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(3), pages 1083-1115, June.
    5. Michael Weber & Ali Ozdagli, 2016. "Monetary Policy Through Production Networks: Evidence from the Stock Market," 2016 Meeting Papers 148, Society for Economic Dynamics.
    6. Sylvain Benoit & Christophe Hurlin & Christophe Perignon, 2015. "Implied Risk Exposures," Review of Finance, European Finance Association, vol. 19(6), pages 2183-2222.
    7. Bernard Herskovic & Bryan Kelly & Hanno Lustig & Stijn Van Nieuwerburgh, 2020. "Firm Volatility in Granular Networks," Journal of Political Economy, University of Chicago Press, vol. 128(11), pages 4097-4162.
    8. Raman Uppal & Harjoat Bhamra, 2016. "Do Individual Behavioral Biases Affect Financial Markets and the Macroeconomy?," 2016 Meeting Papers 1358, Society for Economic Dynamics.
    9. Xinyu Song, 2019. "Large Volatility Matrix Prediction with High-Frequency Data," Papers 1907.01196, arXiv.org, revised Sep 2019.
    10. David Zeke, 2017. "Financial Frictions, Volatility, and Skewness," 2017 Meeting Papers 1421, Society for Economic Dynamics.
    11. Alessandria, George & Choi, Horag & Kaboski, Joseph P. & Midrigan, Virgiliu, 2015. "Microeconomic uncertainty, international trade, and aggregate fluctuations," Journal of Monetary Economics, Elsevier, vol. 69(C), pages 20-38.
    12. Ilze KALNINA & Kokouvi TEWOU, 2015. "Cross-sectional Dependence in Idiosyncratic Volatility," Cahiers de recherche 08-2015, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    13. Stefano Giglio & Bryan Kelly, 2018. "Excess Volatility: Beyond Discount Rates," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 71-127.
    14. Bogdan Wlodarczyk, 2017. "Zmiennosc cen na globalnym rynku surowcow a ryzyko banku," Problemy Zarzadzania, University of Warsaw, Faculty of Management, vol. 15(66), pages 107-124.

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